One simple yet effective way to tackle this problem is to track the minimum and maximum values as we iterate over the arrays, since each array is sorted in ascending order. We initialize the maximum distance as zero. For each array except the first one, calculate the possible distance using the current array's first element with the global maximum, and the current array's last element with the global minimum. Update the global minimum and maximum as you traverse each array. This approach ensures all distance calculations only consider meaningful pairs from different arrays and efficiently computes the result in a single pass.

A naive brute force approach would involve iterating over each pair of arrays and comparing all possible combinations of elements from both arrays. While this method ensures completeness of coverage for all potential maximum distance computations, it results in a less efficient O(m² * n) complexity where n is the average number of elements in one array. It serves as a solid baseline comparison for the more efficient approach.